Developing a resiliency model for survival without major morbidity in preterm infants.

ABSTRACT:

Objective

Develop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates <32 weeks of gestation using machine learning.

Study design

Models using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center.

Results

Discrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882-0.908) for survival and 0.867 (95% CI 0.857-0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the extern

SUBMITTER: Steurer MA 

PROVIDER: S-EPMC10079534 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature